
"""simple docstring"""



import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a       :						Optional[int]       		=		logging.get_logger(__name__)

a       :						List[Any]       		=		'''▁'''

a       :						List[str]       		=		{'''vocab_file''': '''prophetnet.tokenizer'''}

a       :						Optional[Any]       		=		{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

a       :						Optional[Any]       		=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

a       :						Optional[Any]       		=		{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}


def 			_UpperCamelCase						(    _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = collections.OrderedDict()
    with open(_A       ,   """r"""       ,   encoding="""utf-8"""   ) as reader:
        _UpperCAmelCase        = reader.readlines()
    for index, token in enumerate(_A   ):
        _UpperCAmelCase        = token.rstrip("""\n"""   )
        _UpperCAmelCase        = index
    return vocab







class 		a_    (			_UpperCAmelCase						):
    a   :					Union[str, Any]			     =					VOCAB_FILES_NAMES
    a   :					str			     =					PRETRAINED_VOCAB_FILES_MAP
    a   :					str			     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a   :					int			     =					['input_ids', 'attention_mask']







    def __init__(      self      :							int  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							List[Any]="[SEP]"  ,       __UpperCamelCase      :							Tuple="[SEP]"  ,       __UpperCamelCase      :							int="[SEP]"  ,       __UpperCamelCase      :							List[str]="[UNK]"  ,       __UpperCamelCase      :							List[str]="[PAD]"  ,       __UpperCamelCase      :							Any="[CLS]"  ,       __UpperCamelCase      :							Optional[Any]="[MASK]"  ,       __UpperCamelCase      :							Optional[Dict[str, Any]] = None  ,       **__UpperCamelCase      :							Any  ,       )		->None:






        '''simple docstring'''






        _UpperCAmelCase        = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__UpperCamelCase  ,       eos_token=__UpperCamelCase  ,       sep_token=__UpperCamelCase  ,       unk_token=__UpperCamelCase  ,       pad_token=__UpperCamelCase  ,       cls_token=__UpperCamelCase  ,       mask_token=__UpperCamelCase  ,       sp_model_kwargs=self.sp_model_kwargs  ,       **__UpperCamelCase  ,       )

        try:
            import sentencepiece as spm
        except ImportError:
            logger.warning(
                """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
                """ pip install sentencepiece"""  )
            raise

        _UpperCAmelCase        = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.Load(str(__UpperCamelCase  )  )
        _UpperCAmelCase        = vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        _UpperCAmelCase        = {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

        for i in range(10  ):
            _UpperCAmelCase        = f"""[unused{i}]"""
            _UpperCAmelCase        = 5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        _UpperCAmelCase        = 12
        _UpperCAmelCase        = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
            self.unique_no_split_tokens.append(__UpperCamelCase  )







    def __getstate__(      self      :							Any  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.__dict__.copy()
        _UpperCAmelCase        = None
        return state







    def __setstate__(      self      :							List[Any]  ,       __UpperCamelCase      :							List[str]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = d
        try:
            import sentencepiece as spm
        except ImportError:
            logger.warning(
                """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
                """ pip install sentencepiece"""  )
            raise

        # for backward compatibility
        if not hasattr(self  ,       """sp_model_kwargs"""  ):
            _UpperCAmelCase        = {}

        _UpperCAmelCase        = spm.SentencePieceProcessor(**self.sp_model_kwargs  )
        self.sp_model.Load(self.vocab_file  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  ,       __UpperCamelCase      :							bool = False  )		->List[int]:






        '''simple docstring'''






        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__UpperCamelCase  ,       token_ids_a=__UpperCamelCase  ,       already_has_special_tokens=__UpperCamelCase  )

        if token_ids_a is None:
            return ([0] * len(__UpperCamelCase  )) + [1]
        return ([0] * len(__UpperCamelCase  )) + [1] + ([0] * len(__UpperCamelCase  )) + [1]







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [self.sep_token_id]

        if token_ids_a is None:
            return len(token_ids_a + sep  ) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







    @property
    def   _snake_case       (      self      :							Any  )		->Tuple:






        '''simple docstring'''






        return len(self.sp_model  ) + self.fairseq_offset







    def   _snake_case       (      self      :							Any  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = {self.convert_ids_to_tokens(__UpperCamelCase  ): i for i in range(self.vocab_size  )}
        vocab.update(self.added_tokens_encoder  )
        return vocab







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							str  )		->str:






        '''simple docstring'''






        return self.sp_model.encode(__UpperCamelCase  ,       out_type=__UpperCamelCase  )







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							List[Any]  )		->Any:






        '''simple docstring'''






        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        _UpperCAmelCase        = self.sp_model.PieceToId(__UpperCamelCase  )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							Dict  )		->Dict:






        '''simple docstring'''






        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset  )







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							Tuple  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = """""".join(__UpperCamelCase  ).replace(__UpperCamelCase  ,       """ """  ).strip()
        return out_string







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[str] = None  )		->Tuple[str]:






        '''simple docstring'''






        if not os.path.isdir(__UpperCamelCase  ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
            return
        _UpperCAmelCase        = os.path.join(
            __UpperCamelCase  ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

        if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
            copyfile(self.vocab_file  ,       __UpperCamelCase  )
        elif not os.path.isfile(self.vocab_file  ):
            with open(__UpperCamelCase  ,       """wb"""  ) as fi:
                _UpperCAmelCase        = self.sp_model.serialized_model_proto()
                fi.write(__UpperCamelCase  )

        return (out_vocab_file,)







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        if token_ids_a is None:
            return token_ids_a + [self.sep_token_id]
        _UpperCAmelCase        = [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep

"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :						List[Any]       		=		logging.get_logger(__name__)

a       :						str       		=		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					'camembert'







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]=3_05_22  ,       __UpperCamelCase      :							Tuple=7_68  ,       __UpperCamelCase      :							Optional[Any]=12  ,       __UpperCamelCase      :							int=12  ,       __UpperCamelCase      :							int=30_72  ,       __UpperCamelCase      :							int="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							List[Any]=5_12  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Dict=0.0_2  ,       __UpperCamelCase      :							Optional[int]=1e-12  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							int=0  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Optional[int]="absolute"  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							List[str]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       bos_token_id=__UpperCamelCase  ,       eos_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = position_embedding_type
        _UpperCAmelCase        = use_cache
        _UpperCAmelCase        = classifier_dropout







class 		a_    (			_UpperCAmelCase						):







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase        = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase        = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )